Palmprint Recognition Using a Novel Sparse Coding Technique

Authors:
Li Shang;Fenwen Cao;Zhiqiang Zhao;Jie Chen;Yu Zhang
Affiliations:
Dept. of Electronic Information Engineering, Suzhou Vocational University, Jiangsu, 215104, China;Dept. of Electronic Information Engineering, Suzhou Vocational University, Jiangsu, 215104, China;Dept. of Electronic Information Engineering, Suzhou Vocational University, Jiangsu, 215104, China;Dept. of Electronic Information Engineering, Suzhou Vocational University, Jiangsu, 215104, China;Dept. of Electronic Information Engineering, Suzhou Vocational University, Jiangsu, 215104, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

This paper proposes a novel recognition method for palmprints using a new sparse coding (SC) algorithm proposed by us. This algorithm exploited the maximum Kurtosis as the sparse measure criterion, at one time, a fixed variance term of sparse coefficients is used to yield a fixed information capacity. Experimental results show that the feature basis vectors of palmprint images can be successfully extracted by using our SC algorithm. Using the radial basis probabilistic neural network (RBPNN), the classification task can be implemented easily. Finally, compared with methods of principal component analysis (PCA) and the classical SC, simulation results show that our algorithm is indeed efficient and effective in performing palmprint recognition task.